Peak signal-to-noise ratio (PSNR) or mean squared error (MSE) has been frequently used as an objective image quality assessing index when assessing image quality. However, since this image quality assessing index does not easily reflect a subjective image quality of a human, various image quality assessing indexes are being developed. Image quality assessing indexes such as universal quality index (UQI), Structural similarity (SSIM), multi-scale SSIM (MSSSIM), and MSVD have been developed, but these image quality assessing indexes are not easily implemented as hardware and requires a large memory size since they uses floating points when assessing image quality. Therefore, there is needed an image quality assessing technique which may be efficiently implemented as hardware.